Building Predictive Models to Improve Debt Collection Process
  • Key details

Increased revenue 2x times due to improved customer segmentation.

  • Client

The Client is a debt collection agency that collects debts across various industries and customers. The main clients of the agency are banks, retail, telcos, state-owned enterprises.

  • Challenge

Improve debt collection effectiveness with the help of predictive analytics

More than 1500 collection agents across the country deal with approximately 3.5 m debtors per month reaching out to approximately 2 m of them monthly. The debt collection process includes the following steps:

  • connect with an account
  • account verification
  • promise to pay
  • collection

Together with Company`s Head of Data Science, whose department had already initiated implementation of machine learning to improve decision making throughout the collections lifecycle, it was decided that Nobel Link would explore the potential of predictive analytics for identifying those customers who are most likely to repay. The indispensable condition of the assignment was to enable predictions running on the existing client`s MS SQL infrastructure.

  • Solution

Machine learning model for predicting the probability of promise to pay

Nobel Link started working on a machine learning model for predicting the probability of promise to pay from the verified accounts. Accurate predictions should lead to a more prioritized targeting of accounts and thus – improved collection rates and reduced costs. Developing the predictive model included some major steps, such as building a pipeline for data processing and feature creation in SQL Server, training the predictive model based on lightgbm, building a pipeline for getting predictions. The team of a data engineer and a data scientist was assigned to the project, which consisted of the following stages:

  • Result

improved efficiency of the debt collection process

The predictive model delivered by Nobel Link accurately predicts the probability of promise to pay from an account. The model performance was measured by ROC_AUC score. The ROC_AUC score reached ≈0.775, which was a significant improvement for the Client. This gives the Client the ability to optimize collection agents’ time, allowing them to target the most promising accounts first.

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